Estimating the solubility of salsalate in supercritical CO2 via PC-SAFT modeling using its experimental solubility data in organic solvents

In this work, a methodology was studied for estimating the solubility of salsalate, an active pharmaceutical ingredient of non-steroidal anti-inflammatory drugs, in supercritical CO2 (scCO(2)) using the experimental data of its solubility in organic solvents and perturbed-chain statistical associating fluid theory (PC-SAFT)-based modeling. This type of modeling is more predictive than conventional semi-empirical correlation models and cubic-type equations of state. The PC-SAFT pure-component parameters of salsalate were determined by fitting its solubility data in organic solvents, which were newly measured in this work. The PC-SAFT parameters obtained thus were then applied to estimate the solubility of salsalate in scCO(2). This approach can adequately reproduce the experimental values of the isothermal and isobaric solubilities of salsalate in scCO2 over a wide range of temperatures and pressures even when the binary interaction parameter (k(ij)) is set to zero.

Automated summary

In this study, a methodology was developed to estimate the solubility of salsalate in supercritical CO2 using experimental data and PC-SAFT-based modeling. The PC-SAFT parameters were determined by fitting solubility data in organic solvents, and were then applied to estimate solubility in scCO2. This approach accurately predicted solubility over a wide range of conditions.